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Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations

Camburu, O-M; Shillingford, B; Minervini, P; Lukasiewicz, T; Blunsom, P; (2020) Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. (pp. pp. 4157-4165). Association for Computational Linguistics: Stroudsburg, PA, USA. Green open access

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Abstract

To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions. In this work, we show that such models are nonetheless prone to generating mutually inconsistent explanations, such as ”Because there is a dog in the image.” and ”Because there is no dog in the [same] image.”, exposing flaws in either the decision-making process of the model or in the generation of the explanations. We introduce a simple yet effective adversarial framework for sanity checking models against the generation of inconsistent natural language explanations. Moreover, as part of the framework, we address the problem of adversarial attacks with full target sequences, a scenario that was not previously addressed in sequence-to-sequence attacks. Finally, we apply our framework on a state-of-the-art neural natural language inference model that provides natural language explanations for its predictions. Our framework shows that this model is capable of generating a significant number of inconsistent explanations.

Type: Proceedings paper
Title: Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
Event: 58th Annual Meeting of the Association for Computational Linguistics (ACL)
Location: ELECTR NETWORK
Dates: 05 July 2020 - 10 July 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.18653/v1/2020.acl-main.382
Publisher version: https://doi.org/10.18653/v1/2020.acl-main.382
Language: English
Additional information: Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10117599
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